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  <h1>Source code for rfml.nn.train.standard</h1><div class="highlight"><pre>
<span></span><span class="sd">&quot;&quot;&quot;An implementation of a typical multi-class classification training loop.</span>
<span class="sd">&quot;&quot;&quot;</span>
<span class="n">__author__</span> <span class="o">=</span> <span class="s2">&quot;Bryse Flowers &lt;brysef@vt.edu&gt;&quot;</span>

<span class="c1"># External Includes</span>
<span class="kn">from</span> <span class="nn">torch.autograd</span> <span class="k">import</span> <span class="n">Variable</span>
<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="k">import</span> <span class="n">CrossEntropyLoss</span>
<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="k">import</span> <span class="n">Adam</span>
<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="k">import</span> <span class="n">DataLoader</span>

<span class="c1"># Internal Includes</span>
<span class="kn">from</span> <span class="nn">.base</span> <span class="k">import</span> <span class="n">TrainingStrategy</span>
<span class="kn">from</span> <span class="nn">rfml.data</span> <span class="k">import</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">Encoder</span>
<span class="kn">from</span> <span class="nn">rfml.nn.model</span> <span class="k">import</span> <span class="n">Model</span>


<div class="viewcode-block" id="StandardTrainingStrategy"><a class="viewcode-back" href="../../../../nn.html#rfml.nn.train.standard.StandardTrainingStrategy">[docs]</a><span class="k">class</span> <span class="nc">StandardTrainingStrategy</span><span class="p">(</span><span class="n">TrainingStrategy</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;A typical strategy that would be used to train a multi-class classifier.</span>

<span class="sd">    Args:</span>
<span class="sd">        lr (float, optional): Learning rate to be used by the optimizer.</span>
<span class="sd">                              Defaults to 10e-4.</span>
<span class="sd">        max_epochs (int, optional): Maximum number of epochs to train before</span>
<span class="sd">                                    stopping training to preserve computing</span>
<span class="sd">                                    resources (even if the network is still</span>
<span class="sd">                                    improving). Defaults to 50.</span>
<span class="sd">        patience (int, optional): Maximum number of epochs to continue to train</span>
<span class="sd">                                  for even if the network is not still</span>
<span class="sd">                                  improving before deciding that overfitting is</span>
<span class="sd">                                  occurring and stopping. Defaults to 5.</span>
<span class="sd">        batch_size (int, optional): Number of examples to give to the model at</span>
<span class="sd">                                    one time.  If this value is set too high,</span>
<span class="sd">                                    then an out of memory error could occur.  If</span>
<span class="sd">                                    the value is set too low then training will</span>
<span class="sd">                                    take a longer time (and be more variable).</span>
<span class="sd">                                    Defaults to 512.</span>
<span class="sd">        gpu (bool, optional): Flag describing whether the GPU is used or the</span>
<span class="sd">                              training is performed wholly on the CPU.</span>
<span class="sd">                              Defaults to True.</span>
<span class="sd">    &quot;&quot;&quot;</span>

    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span>
        <span class="n">lr</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">10e-4</span><span class="p">,</span>
        <span class="n">max_epochs</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span>
        <span class="n">patience</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
        <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span>
        <span class="n">gpu</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
    <span class="p">):</span>
        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="k">if</span> <span class="n">lr</span> <span class="o">&lt;=</span> <span class="mf">0.0</span> <span class="ow">or</span> <span class="n">lr</span> <span class="o">&gt;=</span> <span class="mf">1.0</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;A sane human would choose a learning rate between 0-1, but, you chose &quot;</span>
                <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lr</span><span class="p">)</span>
            <span class="p">)</span>
        <span class="k">if</span> <span class="n">max_epochs</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;You must train for at least 1 epoch, you set the max epochs as &quot;</span>
                <span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">max_epochs</span><span class="p">)</span>
            <span class="p">)</span>
        <span class="k">if</span> <span class="n">patience</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;Patience must be a positive integer, not </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">patience</span><span class="p">)</span>
            <span class="p">)</span>
        <span class="k">if</span> <span class="n">batch_size</span> <span class="o">&lt;</span> <span class="mi">1</span><span class="p">:</span>
            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
                <span class="s2">&quot;The batch size must be a positive integer, not </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">batch_size</span><span class="p">)</span>
            <span class="p">)</span>

        <span class="bp">self</span><span class="o">.</span><span class="n">lr</span> <span class="o">=</span> <span class="n">lr</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">max_epochs</span> <span class="o">=</span> <span class="n">max_epochs</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">patience</span> <span class="o">=</span> <span class="n">patience</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">gpu</span> <span class="o">=</span> <span class="n">gpu</span>

    <span class="k">def</span> <span class="nf">__call__</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">Model</span><span class="p">,</span> <span class="n">training</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">validation</span><span class="p">:</span> <span class="n">Dataset</span><span class="p">,</span> <span class="n">le</span><span class="p">:</span> <span class="n">Encoder</span>
    <span class="p">):</span>
        <span class="sd">&quot;&quot;&quot;Train the model on the provided training data.</span>

<span class="sd">        Args:</span>
<span class="sd">            model (Model): Model to fit to the training data.</span>
<span class="sd">            training (Dataset): Data used in the training loop.</span>
<span class="sd">            validation (Dataset): Data only used for early stopping validation.</span>
<span class="sd">            le (Encoder): Mapping from human readable labels to model readable.</span>
<span class="sd">        &quot;&quot;&quot;</span>
        <span class="c1"># The PyTorch implementation of CrossEntropyLoss combines the softmax</span>
        <span class="c1"># and natural log likelihood loss into one (so none of the models</span>
        <span class="c1"># should have softmax included in them)</span>
        <span class="n">criterion</span> <span class="o">=</span> <span class="n">CrossEntropyLoss</span><span class="p">()</span>

        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpu</span><span class="p">:</span>
            <span class="n">model</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
            <span class="n">criterion</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

        <span class="n">optimizer</span> <span class="o">=</span> <span class="n">Adam</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">lr</span><span class="p">)</span>

        <span class="n">train_data</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
            <span class="n">training</span><span class="o">.</span><span class="n">as_torch</span><span class="p">(</span><span class="n">le</span><span class="o">=</span><span class="n">le</span><span class="p">),</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
        <span class="p">)</span>
        <span class="n">val_data</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span>
            <span class="n">validation</span><span class="o">.</span><span class="n">as_torch</span><span class="p">(</span><span class="n">le</span><span class="o">=</span><span class="n">le</span><span class="p">),</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span>
        <span class="p">)</span>

        <span class="c1"># Fit the data for the maximum number of epochs, bailing out early if</span>
        <span class="c1"># the early stopping condition is reached.  Set the initial &quot;best&quot; very</span>
        <span class="c1"># high so the first epoch is always an improvement</span>
        <span class="n">best_val_loss</span> <span class="o">=</span> <span class="mf">10e10</span>
        <span class="n">epochs_since_best</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="n">best_epoch</span> <span class="o">=</span> <span class="mi">0</span>
        <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_epochs</span><span class="p">):</span>
            <span class="n">train_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_train_one_epoch</span><span class="p">(</span>
                <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">train_data</span><span class="p">,</span> <span class="n">loss_fn</span><span class="o">=</span><span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="n">optimizer</span>
            <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_dispatch_epoch_completed</span><span class="p">(</span><span class="n">mean_loss</span><span class="o">=</span><span class="n">train_loss</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="n">epoch</span><span class="p">)</span>

            <span class="n">val_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_validate_once</span><span class="p">(</span>
                <span class="n">model</span><span class="o">=</span><span class="n">model</span><span class="p">,</span> <span class="n">data</span><span class="o">=</span><span class="n">val_data</span><span class="p">,</span> <span class="n">loss_fn</span><span class="o">=</span><span class="n">criterion</span>
            <span class="p">)</span>
            <span class="bp">self</span><span class="o">.</span><span class="n">_dispatch_validation_completed</span><span class="p">(</span><span class="n">mean_loss</span><span class="o">=</span><span class="n">val_loss</span><span class="p">,</span> <span class="n">epoch</span><span class="o">=</span><span class="n">epoch</span><span class="p">)</span>

            <span class="k">if</span> <span class="n">val_loss</span> <span class="o">&lt;</span> <span class="n">best_val_loss</span><span class="p">:</span>
                <span class="n">best_val_loss</span> <span class="o">=</span> <span class="n">val_loss</span>
                <span class="n">epochs_since_best</span> <span class="o">=</span> <span class="mi">0</span>
                <span class="n">best_epoch</span> <span class="o">=</span> <span class="n">epoch</span>
                <span class="n">model</span><span class="o">.</span><span class="n">save</span><span class="p">()</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">epochs_since_best</span> <span class="o">+=</span> <span class="mi">1</span>

            <span class="k">if</span> <span class="n">epochs_since_best</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">patience</span><span class="p">:</span>
                <span class="k">break</span>

        <span class="c1"># Reload the &quot;best&quot; weights</span>
        <span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">_dispatch_training_completed</span><span class="p">(</span>
            <span class="n">best_loss</span><span class="o">=</span><span class="n">best_val_loss</span><span class="p">,</span> <span class="n">best_epoch</span><span class="o">=</span><span class="n">best_epoch</span><span class="p">,</span> <span class="n">total_epochs</span><span class="o">=</span><span class="n">epoch</span>
        <span class="p">)</span>

    <span class="k">def</span> <span class="nf">_train_one_epoch</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">Model</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">:</span> <span class="n">CrossEntropyLoss</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">:</span> <span class="n">Adam</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
        <span class="n">total_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="c1"># Switch the model mode so it remembers gradients, induces dropout, etc.</span>
        <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>

        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
            <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">batch</span>

            <span class="c1"># Push data to GPU</span>
            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpu</span><span class="p">:</span>
                <span class="n">x</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">cuda</span><span class="p">())</span>
                <span class="n">y</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">y</span><span class="o">.</span><span class="n">cuda</span><span class="p">())</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="n">x</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
                <span class="n">y</span> <span class="o">=</span> <span class="n">Variable</span><span class="p">(</span><span class="n">y</span><span class="p">)</span>

            <span class="c1"># Forward pass of prediction</span>
            <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

            <span class="c1"># Zero out the parameter gradients, because they are cumulative,</span>
            <span class="c1"># compute loss, compute gradients (backward), update weights</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
            <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
            <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
            <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>

            <span class="n">total_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

        <span class="n">mean_loss</span> <span class="o">=</span> <span class="n">total_loss</span> <span class="o">/</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">mean_loss</span>

    <span class="k">def</span> <span class="nf">_validate_once</span><span class="p">(</span>
        <span class="bp">self</span><span class="p">,</span> <span class="n">model</span><span class="p">:</span> <span class="n">Model</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">DataLoader</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">:</span> <span class="n">CrossEntropyLoss</span>
    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
        <span class="n">total_loss</span> <span class="o">=</span> <span class="mf">0.0</span>
        <span class="c1"># Switch the model back to test mode (so that batch norm/dropout doesn&#39;t</span>
        <span class="c1"># take effect)</span>
        <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
            <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">batch</span>

            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">gpu</span><span class="p">:</span>
                <span class="n">x</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>
                <span class="n">y</span> <span class="o">=</span> <span class="n">y</span><span class="o">.</span><span class="n">cuda</span><span class="p">()</span>

            <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
            <span class="n">loss</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
            <span class="n">total_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>

        <span class="n">mean_loss</span> <span class="o">=</span> <span class="n">total_loss</span> <span class="o">/</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mf">1.0</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">mean_loss</span></div>
</pre></div>

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